From aircraft tracking data to network delay model: A data-driven approach considering en-route congestion. (October 2021)

• Record Type: Journal Article
• Authors: Lin, Yu; Li, Lishuai; Ren, Pan; Wang, Yanjun; Szeto, W.Y.
• Is Part Of: Transportation research. Volume 131(2021)
• Publication Date: 2021-10
• Subjects: En-route congestion -- Trajectory clustering -- Queuing network -- Flight delay; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2021.103329
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: A network flight delay model, MATND, is developed to consider en-route congestions. MATND takes aircraft tracking data to characterize a national air traffic network. MATND calculates flight delays and tracks propagations over an air traffic network. Theoretical analysis and simulations are made to show the advantages of MATND. MATND is good for what-if analysis of improvement strategies at a macroscopic level. Abstract: En-route congestion causes delays in air traffic networks and will become more prominent as air traffic demand will continue to increase yet airspace volume cannot grow. However, most existing studies on flight delay modeling do not consider en-route congestion explicitly. In this study, we propose a new flight delay model, Multi-layer Air Traffic Network Delay (MATND) model, to capture the impact of en-route congestion on flight delays over an air traffic network. This model is developed by a data-driven approach, taking aircraft tracking data and flight schedules as inputs to characterize a national air traffic network, as well as a system-level model approach, modeling the delay process based on queueing theory. The two approaches combined make the network delay model a close representation of reality and easy-to-implement for what-if scenario analysis. The proposed MATND model includes 1) a data-driven method to learn a network composed of airports, en-route congestion points, and air corridors from aircraft tracking data, 2) a stochastic and dynamic queuing network model to calculate flight delays and track their propagation at both airports and in en-route congestion areas, in which the delays are computed via a space–time decomposition method. Using one month of historical aircraft tracking data over China's air traffic network, MATND is tested and shows to give an accurate quantification of delays of the national air traffic network. "What-if" scenario analyses are conducted to demonstrate how the proposed model can be used for the evaluation of air traffic network improvement strategies, where the manipulation of reality at such a scale is impossible. Results show that MATND is computationally efficient, well suited for evaluating the impact of policy alternatives on system-wide delay at a macroscopic level.